Richard A Deyo1,2,3, Sara E Hallvik4, Christi Hildebran4, Miguel Marino5,6, Eve Dexter5, Jessica M Irvine4,7, Nicole O'Kane4, Joshua Van Otterloo8, Dagan A Wright8, Gillian Leichtling4, Lisa M Millet8. 1. Department of Family Medicine, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Mail Code FM, Portland, OR, 97239, USA. deyor@ohsu.edu. 2. Department of Public Health and Preventive Medicine, Oregon Health and Science University, Portland, OR, USA. deyor@ohsu.edu. 3. Department of Medicine and The Oregon Institute for Occupational Health Sciences, Oregon Health and Science University, Portland, OR, USA. deyor@ohsu.edu. 4. Acumentra Health, Portland, OR, USA. 5. Department of Family Medicine, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Mail Code FM, Portland, OR, 97239, USA. 6. Department of Public Health and Preventive Medicine, Oregon Health and Science University, Portland, OR, USA. 7. OCHIN Inc., Portland, OR, USA. 8. Injury and Violence Prevention Program for the State of Oregon, Portland, OR, USA.
Abstract
BACKGROUND: Long-term efficacy of opioids for non-cancer pain is unproven, but risks argue for cautious prescribing. Few data suggest how long or how much opioid can be prescribed for opioid-naïve patients without inadvertently promoting long-term use. OBJECTIVE: To examine the association between initial opioid prescribing patterns and likelihood of long-term use among opioid-naïve patients. DESIGN: Retrospective cohort study; data from Oregon resident prescriptions linked to death certificates and hospital discharges. PARTICIPANTS: Patients filling opioid prescriptions between October 1, 2012, and September 30, 2013, with no opioid fills for the previous 365 days. Subgroup analyses examined patients under age 45 who did not die in the follow-up year, excluding most cancer or palliative care patients. MAIN MEASURES: Exposure: Numbers of prescription fills and cumulative morphine milligram equivalents (MMEs) dispensed during 30 days following opioid initiation ("initiation month"). OUTCOME: Proportion of patients with six or more opioid fills during the subsequent year ("long-term users"). KEY RESULTS: There were 536,767 opioid-naïve patients who filled an opioid prescription. Of these, 26,785 (5.0 %) became long-term users. Numbers of fills and cumulative MMEs during the initiation month were associated with long-term use. Among patients under age 45 using short-acting opioids who did not die in the follow-up year, the adjusted odds ratio (OR) for long-term use among those receiving two fills versus one was 2.25 (95 % CI: 2.17, 2.33). Compared to those who received < 120 total MMEs, those who received between 400 and 799 had an OR of 2.96 (95 % CI: 2.81, 3.11). Patients initiating with long-acting opioids had a higher risk of long-term use than those initiating with short-acting drugs. CONCLUSIONS: Early opioid prescribing patterns are associated with long-term use. While patient characteristics are important, clinicians have greater control over initial prescribing. Our findings may help minimize the risk of inadvertently initiating long-term opioid use.
BACKGROUND: Long-term efficacy of opioids for non-cancer pain is unproven, but risks argue for cautious prescribing. Few data suggest how long or how much opioid can be prescribed for opioid-naïve patients without inadvertently promoting long-term use. OBJECTIVE: To examine the association between initial opioid prescribing patterns and likelihood of long-term use among opioid-naïve patients. DESIGN: Retrospective cohort study; data from Oregon resident prescriptions linked to death certificates and hospital discharges. PARTICIPANTS: Patients filling opioid prescriptions between October 1, 2012, and September 30, 2013, with no opioid fills for the previous 365 days. Subgroup analyses examined patients under age 45 who did not die in the follow-up year, excluding most cancer or palliative care patients. MAIN MEASURES: Exposure: Numbers of prescription fills and cumulative morphine milligram equivalents (MMEs) dispensed during 30 days following opioid initiation ("initiation month"). OUTCOME: Proportion of patients with six or more opioid fills during the subsequent year ("long-term users"). KEY RESULTS: There were 536,767 opioid-naïve patients who filled an opioid prescription. Of these, 26,785 (5.0 %) became long-term users. Numbers of fills and cumulative MMEs during the initiation month were associated with long-term use. Among patients under age 45 using short-acting opioids who did not die in the follow-up year, the adjusted odds ratio (OR) for long-term use among those receiving two fills versus one was 2.25 (95 % CI: 2.17, 2.33). Compared to those who received < 120 total MMEs, those who received between 400 and 799 had an OR of 2.96 (95 % CI: 2.81, 3.11). Patients initiating with long-acting opioids had a higher risk of long-term use than those initiating with short-acting drugs. CONCLUSIONS: Early opioid prescribing patterns are associated with long-term use. While patient characteristics are important, clinicians have greater control over initial prescribing. Our findings may help minimize the risk of inadvertently initiating long-term opioid use.
Entities:
Keywords:
opioid initiation; opioid-naïve; opioids; pain; prescription drug monitoring programs
Authors: Teryl K Nuckols; Laura Anderson; Ioana Popescu; Allison L Diamant; Brian Doyle; Paul Di Capua; Roger Chou Journal: Ann Intern Med Date: 2014-01-07 Impact factor: 25.391
Authors: Matthew Miller; Catherine W Barber; Sarah Leatherman; Jennifer Fonda; John A Hermos; Kelly Cho; David R Gagnon Journal: JAMA Intern Med Date: 2015-04 Impact factor: 21.873
Authors: W Michael Hooten; Jennifer L St Sauver; Michaela E McGree; Debra J Jacobson; David O Warner Journal: Mayo Clin Proc Date: 2015-07 Impact factor: 7.616
Authors: Nancy E Morden; Jeffrey C Munson; Carrie H Colla; Jonathan S Skinner; Julie P W Bynum; Weiping Zhou; Ellen Meara Journal: Med Care Date: 2014-09 Impact factor: 2.983
Authors: Leonard J Paulozzi; Edwin M Kilbourne; Nina G Shah; Kurt B Nolte; Hema A Desai; Michael G Landen; William Harvey; Larry D Loring Journal: Pain Med Date: 2011-10-25 Impact factor: 3.750
Authors: Roger Chou; Gilbert J Fanciullo; Perry G Fine; Jeremy A Adler; Jane C Ballantyne; Pamela Davies; Marilee I Donovan; David A Fishbain; Kathy M Foley; Jeffrey Fudin; Aaron M Gilson; Alexander Kelter; Alexander Mauskop; Patrick G O'Connor; Steven D Passik; Gavril W Pasternak; Russell K Portenoy; Ben A Rich; Richard G Roberts; Knox H Todd; Christine Miaskowski Journal: J Pain Date: 2009-02 Impact factor: 5.820
Authors: Richard A Deyo; Jessica M Irvine; Lisa M Millet; Todd Beran; Nicole O'Kane; Dagan A Wright; Dennis McCarty Journal: Health Aff (Millwood) Date: 2013-02-13 Impact factor: 6.301
Authors: Kevin C Abbott; Chyng-Wen Fwu; Paul W Eggers; Anne W Eggers; Prudence P Kline; Paul L Kimmel Journal: Transplantation Date: 2018-06 Impact factor: 4.939
Authors: Jessica D McDermott; Megan Eguchi; William A Stokes; Arya Amini; Mohammad Hararah; Ding Ding; Allison Valentine; Cathy J Bradley; Sana D Karam Journal: Otolaryngol Head Neck Surg Date: 2018-11-06 Impact factor: 3.497
Authors: Chad M Brummett; Jennifer F Waljee; Jenna Goesling; Stephanie Moser; Paul Lin; Michael J Englesbe; Amy S B Bohnert; Sachin Kheterpal; Brahmajee K Nallamothu Journal: JAMA Surg Date: 2017-06-21 Impact factor: 14.766
Authors: Ruchir N Karmali; Christopher Bush; Sudha R Raman; Cynthia I Campbell; Asheley C Skinner; Andrew W Roberts Journal: Pharmacoepidemiol Drug Saf Date: 2019-12-18 Impact factor: 2.890
Authors: Lisa X Deng; Kanan Patel; Christine Miaskowski; Ingrid Maravilla; Sarah Schear; Sarah Garrigues; Nicole Thompson; Andrew D Auerbach; Christine S Ritchie Journal: J Am Geriatr Soc Date: 2018-08-10 Impact factor: 5.562